Further Reading: Descriptive Statistics in Football

Foundational Statistics Textbooks

For Beginners

  1. "Statistics" by David Freedman, Robert Pisani, and Roger Purves - Classic introduction with intuitive explanations - Strong emphasis on understanding concepts before formulas - Excellent for building statistical intuition - ISBN: 978-0393929720

  2. "Naked Statistics" by Charles Wheelan - Accessible, entertaining introduction to statistics - Real-world examples throughout - Great for understanding why statistics matter - ISBN: 978-0393347777

  3. "The Art of Statistics" by David Spiegelhalter - Modern approach to statistical thinking - Focuses on interpretation and communication - Excellent data visualization examples - ISBN: 978-1541618510

For Deeper Understanding

  1. "All of Statistics" by Larry Wasserman - Comprehensive coverage of statistical methods - More mathematical rigor - Good reference for advanced techniques - ISBN: 978-0387402727

  2. "Introduction to Statistical Learning" by James, Witten, Hastie, Tibshirani - Bridges descriptive and predictive statistics - Excellent R and Python applications - Free PDF available: https://www.statlearning.com - ISBN: 978-1071614174

Sports Analytics Literature

Books

  1. "Mathletics" by Wayne Winston - Comprehensive sports analytics textbook - Covers multiple sports including football - Strong quantitative foundation - ISBN: 978-0691177625

  2. "Analyzing Baseball Data with R" by Marchi and Albert - While baseball-focused, teaches transferable skills - Excellent examples of descriptive statistics in sports - Strong R programming instruction - ISBN: 978-0815353515

  3. "Football Analytics with Python & R" by Eric Eager and Richard Erickson - Directly applicable to NFL analytics - Modern analytical techniques - Comprehensive code examples - ISBN: 978-1492099611

Academic Papers

  1. "Expected Points and EPA Explained" - nflfastR Documentation - Foundation for understanding modern football metrics - https://www.nflfastr.com/articles/beginners_guide.html

  2. "A New Way to Measure Clutch" by Bill Barnwell - Application of z-scores to performance evaluation - ESPN Analytics methodology

  3. "Consistency and the NFL" by Chase Stuart - Analysis of game-to-game variation - Football Perspective archives

Online Resources

Websites and Blogs

  1. Football Outsiders (https://www.footballoutsiders.com) - Pioneer in advanced football statistics - DVOA methodology explained - Regular statistical analysis articles

  2. ESPN Stats & Information (https://www.espn.com/blog/statsinfo) - Professional sports statistics - QBR and other proprietary metrics explained

  3. Pro Football Reference (https://www.pro-football-reference.com) - Comprehensive statistical database - Glossary of statistics - Historical comparisons

  4. College Football Reference (https://www.sports-reference.com/cfb/) - Complete college football statistics - School and player histories - Advanced metrics

  5. Bill Connelly's Work (ESPN/SB Nation archives) - SP+ rating system - Five Factors of football - Extensive college football analysis

Python/Pandas Resources

  1. Pandas Documentation (https://pandas.pydata.org/docs/) - Official documentation - Descriptive statistics methods: .describe(), .corr(), .agg() - GroupBy operations

  2. SciPy Stats Tutorial (https://docs.scipy.org/doc/scipy/tutorial/stats.html) - Statistical functions in Python - Distribution fitting - Hypothesis testing

  3. Real Python Statistics Tutorials (https://realpython.com/python-statistics/) - Practical Python statistics guides - Step-by-step examples - Best practices

Data Sources

Free College Football Data

  1. CollegeFootballData.com API (https://collegefootballdata.com) - Comprehensive play-by-play data - Team and player statistics - Free API with reasonable limits - Python wrapper: cfbd package

  2. cfbfastR (https://cfbfastr.sportsdataverse.org/) - R package for college football data - Based on ESPN and CFBD data - Python equivalent: cfbfastR-py

  3. Sports Reference Data (https://www.sports-reference.com) - Historical statistics - CSV export available - Extensive school-by-school data

Commercial/Professional Sources

  1. Pro Football Focus (PFF) - Detailed player grades - Play-by-play charting - Subscription required for full access

  2. Sports Info Solutions (SIS) - Advanced tracking data - Professional analytics services

  3. Second Spectrum / NFL Next Gen Stats - Player tracking data - Professional use primarily

Video Resources

Courses

  1. Khan Academy Statistics (https://www.khanacademy.org/math/statistics-probability) - Free, comprehensive statistics course - Interactive exercises - Video explanations

  2. Coursera: Statistics with Python (University of Michigan) - Applied statistics using Python - pandas and scipy focus - Certificate available

  3. DataCamp: Statistical Thinking (https://www.datacamp.com) - Interactive Python-based learning - Sports analytics tracks available - Subscription-based

YouTube Channels

  1. StatQuest with Josh Starmer - Clear explanations of statistical concepts - Visual approach to learning - https://www.youtube.com/user/joshstarmer

  2. 3Blue1Brown - Mathematical intuition - Excellent visualizations - https://www.youtube.com/c/3blue1brown

  3. Sports Analytics Research - MIT Sloan Sports Analytics Conference talks - Available on YouTube - Professional presentations

Practice Datasets

  1. NFL Play-by-Play (nflfastR) - Complete NFL play data - EPA and WPA calculated - https://github.com/nflverse/nflverse-data

  2. CFB Play-by-Play (cfbfastR) - College football play data - 2014-present seasons - https://github.com/sportsdataverse/cfbfastR-data

  3. Kaggle NFL Datasets - Various competition datasets - Tracking data available - https://www.kaggle.com/datasets?search=nfl

  4. FiveThirtyEight Sports Data - Curated sports datasets - Methodology documentation - https://github.com/fivethirtyeight/data

Conferences and Community

Academic/Professional Conferences

  1. MIT Sloan Sports Analytics Conference - Premier sports analytics conference - Research paper competitions - https://www.sloansportsconference.com

  2. SABR Analytics Conference - Society for American Baseball Research - Increasingly multi-sport - Statistical methodology focus

  3. useR! and PyCon - R and Python conferences - Sports analytics tracks - Open source community

Online Communities

  1. r/CFBAnalysis (Reddit) - College football analytics discussion - Data sharing - Project collaboration

  2. Sports Analytics Twitter/X - Key accounts: @benbbaldwin, @PFF_College, @ESPN_Analytics - Real-time discussion - New research sharing

  3. Discord Communities - nflverse Discord for NFL data - sportsdataverse for college sports - Active developer communities

Suggested Learning Path

Week 1-2: Statistics Foundations

  • Read: "Naked Statistics" Chapters 1-5
  • Practice: Khan Academy descriptive statistics
  • Apply: Calculate team statistics with pandas

Week 3-4: Sports Applications

  • Read: Football Outsiders methodology articles
  • Practice: Analyze one season of team data
  • Apply: Build correlation analysis of stats vs. wins

Week 5-6: Advanced Techniques

  • Read: "Mathletics" football chapters
  • Practice: Z-score player comparisons
  • Apply: Create player consistency analysis

Week 7-8: Integration

  • Read: Bill Connelly's SP+ explanation
  • Practice: Reproduce a published analysis
  • Apply: Present findings with visualizations

Citation Format

When using statistical concepts in your own work, consider citing:

For foundational statistics:
Freedman, D., Pisani, R., & Purves, R. (2007). Statistics (4th ed.).
W. W. Norton & Company.

For sports analytics methodology:
Winston, W. L. (2012). Mathletics: How Gamblers, Managers, and Sports
Enthusiasts Use Mathematics in Baseball, Basketball, and Football.
Princeton University Press.

For college football data:
CollegeFootballData.com. (Year). [Dataset name]. Retrieved from
https://collegefootballdata.com